


"""simple docstring"""


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCamelCase   :List[Any]						     =		200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCamelCase   :Tuple						     =		50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCamelCase   :Dict						     =		0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))




def 	snake_case		(  UpperCamelCase__							:    str      ,					UpperCamelCase__							:    str     ) ->   tuple[str, float]:
      lowerCamelCase       :       List[str]   =						len([g for position, g in enumerate(_lowerCamelCase     ) if g == main_target[position]]     )
      return (item, float(_lowerCamelCase     ))




def 	snake_case		(  UpperCamelCase__							:    str      ,					UpperCamelCase__							:    str     ) ->   tuple[str, str]:
      lowerCamelCase       :       Optional[Any]   =						random.randint(0      ,					len(_lowerCamelCase     ) - 1     )
      lowerCamelCase       :       Dict   =						parent_a[:random_slice] + parent_a[random_slice:]
      lowerCamelCase       :       Tuple   =						parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)




def 	snake_case		(  UpperCamelCase__							:    str      ,					UpperCamelCase__							:    list[str]     ) ->   str:
      lowerCamelCase       :       Union[str, Any]   =						list(_lowerCamelCase     )
      if random.uniform(0      ,					1     ) < MUTATION_PROBABILITY:
            lowerCamelCase       :       Tuple   =						random.choice(_lowerCamelCase     )
      return "".join(_lowerCamelCase     )




def 	snake_case		(  UpperCamelCase__							:    tuple[str, float]      ,					UpperCamelCase__							:    list[tuple[str, float]]      ,					UpperCamelCase__							:    list[str]      ,					) ->   list[str]:
      lowerCamelCase       :       Optional[int]   =						[]
      # Generate more children proportionally to the fitness score.
      lowerCamelCase       :       Optional[int]   =						int(parent_a[1] * 100     ) + 1
      lowerCamelCase       :       Dict   =						10 if child_n >= 10 else child_n
      for _ in range(_lowerCamelCase     ):
            lowerCamelCase       :       List[Any]   =						population_score[random.randint(0      ,					_lowerCamelCase     )][0]

            lowerCamelCase  ,      lowerCamelCase       :       List[Any]   =						crossover(parent_a[0]      ,					_lowerCamelCase     )
            # Append new string to the population list.
            pop.append(mutate(_lowerCamelCase      ,					_lowerCamelCase     )     )
            pop.append(mutate(_lowerCamelCase      ,					_lowerCamelCase     )     )
      return pop




def 	snake_case		(  UpperCamelCase__							:    str      ,					UpperCamelCase__							:    list[str]      ,					UpperCamelCase__							:    bool = True     ) ->   tuple[int, int, str]:

      # Verify if N_POPULATION is bigger than N_SELECTED
      if N_POPULATION < N_SELECTED:
            lowerCamelCase       :       List[Any]   =						F'{N_POPULATION} must be bigger than {N_SELECTED}'
            raise ValueError(_lowerCamelCase     )
      # Verify that the target contains no genes besides the ones inside genes variable.
      lowerCamelCase       :       List[Any]   =						sorted({c for c in target if c not in genes}     )
      if not_in_genes_list:
            lowerCamelCase       :       Tuple   =						F'{not_in_genes_list} is not in genes list, evolution cannot converge'
            raise ValueError(_lowerCamelCase     )

      # Generate random starting population.
      lowerCamelCase       :       Union[str, Any]   =						[]
      for _ in range(_lowerCamelCase     ):
            population.append("""""".join([random.choice(_lowerCamelCase     ) for i in range(len(_lowerCamelCase     )     )]     )     )

      # Just some logs to know what the algorithms is doing.
      lowerCamelCase  ,      lowerCamelCase       :       int   =						0, 0

      # This loop will end when we find a perfect match for our target.
      while True:
            generation += 1
            total_population += len(_lowerCamelCase     )

            # Random population created. Now it's time to evaluate.

            # Adding a bit of concurrency can make everything faster,
            #
            # import concurrent.futures
            # population_score: list[tuple[str, float]] = []
            # with concurrent.futures.ThreadPoolExecutor(
            #                                   max_workers=NUM_WORKERS) as executor:
            #     futures = {executor.submit(evaluate, item) for item in population}
            #     concurrent.futures.wait(futures)
            #     population_score = [item.result() for item in futures]
            #
            # but with a simple algorithm like this, it will probably be slower.
            # We just need to call evaluate for every item inside the population.
            lowerCamelCase       :       Union[str, Any]   =						[evaluate(_lowerCamelCase      ,					_lowerCamelCase     ) for item in population]

            # Check if there is a matching evolution.
            lowerCamelCase       :       int   =						sorted(_lowerCamelCase      ,					key=lambda UpperCamelCase__     : x[1]      ,					reverse=_lowerCamelCase     )
            if population_score[0][0] == target:
                  return (generation, total_population, population_score[0][0])

            # Print the best result every 10 generation.
            # Just to know that the algorithm is working.
            if debug and generation % 10 == 0:
                  print(
                      F'\nGeneration: {generation}'
                      F'\nTotal Population:{total_population}'
                      F'\nBest score: {population_score[0][1]}'
                      F'\nBest string: {population_score[0][0]}'     )

            # Flush the old population, keeping some of the best evolutions.
            # Keeping this avoid regression of evolution.
            lowerCamelCase       :       Dict   =						population[: int(N_POPULATION / 3     )]
            population.clear()
            population.extend(_lowerCamelCase     )
            # Normalize population score to be between 0 and 1.
            lowerCamelCase       :       Optional[int]   =						[
                (item, score / len(_lowerCamelCase     )) for item, score in population_score
            ]

            # This is selection
            for i in range(_lowerCamelCase     ):
                  population.extend(select(population_score[int(_lowerCamelCase     )]      ,					_lowerCamelCase      ,					_lowerCamelCase     )     )
                  # Check if the population has already reached the maximum value and if so,
                  # break the cycle.  If this check is disabled, the algorithm will take
                  # forever to compute large strings, but will also calculate small strings in
                  # a far fewer generations.
                  if len(_lowerCamelCase     ) > N_POPULATION:
                        break


if __name__ == "__main__":
    __lowerCamelCase   :Any						     =		(
        '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
    )
    __lowerCamelCase   :Optional[int]						     =		list(
        ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
        'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
    )
    __lowerCamelCase   :str						     =		basic(target_str, genes_list)
    print(
        f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
    )












"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




